The representation of deep convection in general circulation models is in part informed by cloud-resolving models (CRMs) that function at higher spatial and temporal resolution; however, recent studies have shown that CRMs often fail at capturing the details of deep convection updrafts. With the goal of providing constraint on CRM simulation of deep convection updrafts, ground-based remote-sensing observations are analyzed and statistically correlated for four deep convection events observed during the Midlatitude Continental Convective Clouds Experiment (MC3E). Since positive values of specific differential phase (KDP) are associated with deep convection updraft cells, the volume of KDPgreater than a set threshold is quantified as a function of height, and KDP columns are also detected as contiguous regions that extend above the melting level using two radars in Oklahoma: the National Weather Service Vance WSR-88D (KVNX) and the Department of Energy C-band Scanning Atmospheric Radiation Measurement (ARM) Precipitation Radar (C-SAPR). KVNX and C-SAPR KDP volumes and columns are then statistically correlated with vertical winds retrieved via multi-Doppler wind analysis, lightning flash activity derived from the Oklahoma Lightning Mapping Array, and KVNX differential reflectivity (ZDR). Results indicate strong correlations of KDP volume above the melting level with updraft mass flux, lightning flash activity, and intense rainfall. Analysis of KDP columns reveals signatures of changing updraft properties from one storm event to another as well as during event evolution. Comparison of ZDR to KDP shows commonalities in information content of each, as well as potential problems with ZDR associated with observational artifacts.